Overview

Dataset statistics

Number of variables13
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory324.7 KiB
Average record size in memory112.0 B

Variable types

Numeric13

Alerts

monetary is highly overall correlated with qtde_invoices and 3 other fieldsHigh correlation
recency is highly overall correlated with qtde_invoicesHigh correlation
qtde_invoices is highly overall correlated with monetary and 3 other fieldsHigh correlation
qtde_items is highly overall correlated with monetary and 3 other fieldsHigh correlation
qtde_products is highly overall correlated with monetary and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
diff_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with diff_daysHigh correlation
qtdade_itens_retornados is highly overall correlated with num_retornosHigh correlation
num_retornos is highly overall correlated with qtdade_itens_retornadosHigh correlation
avg_basket_size is highly overall correlated with monetary and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qtde_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.4442279)Skewed
frequency is highly skewed (γ1 = 24.88037069)Skewed
qtdade_itens_retornados is highly skewed (γ1 = 51.79774426)Skewed
avg_basket_size is highly skewed (γ1 = 44.68328098)Skewed
customer_id has unique valuesUnique
recency has 34 (1.1%) zerosZeros
qtdade_itens_retornados has 1481 (49.9%) zerosZeros
num_retornos has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-04-29 12:10:21.366854
Analysis finished2023-04-29 12:10:57.167404
Duration35.8 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:10:57.278739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2023-04-29T09:10:57.503438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

monetary
Real number (ℝ)

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.2261
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:10:57.699963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.491
Coefficient of variation (CV)3.8485342
Kurtosis353.95857
Mean2749.2261
Median Absolute Deviation (MAD)672.72
Skewness16.777879
Sum8162452.2
Variance1.1194678 × 108
MonotonicityNot monotonic
2023-04-29T09:10:57.865632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140438.72 1
< 0.1%
124564.53 1
< 0.1%
117375.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.288649
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:10:58.118652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756171
Coefficient of variation (CV)1.2094852
Kurtosis2.7780386
Mean64.288649
Median Absolute Deviation (MAD)26
Skewness1.7983969
Sum190873
Variance6046.0221
MonotonicityNot monotonic
2023-04-29T09:10:58.329332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
22 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtde_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7228023
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:10:58.553473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8566539
Coefficient of variation (CV)1.5476079
Kurtosis190.82536
Mean5.7228023
Median Absolute Deviation (MAD)2
Skewness10.766456
Sum16991
Variance78.440319
MonotonicityNot monotonic
2023-04-29T09:10:58.772269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 786
26.5%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 786
26.5%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtde_items
Real number (ℝ)

Distinct1665
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1606.4611
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:10:58.982640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.4
Q1296
median639
Q31399
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation5882.9765
Coefficient of variation (CV)3.6620722
Kurtosis467.15372
Mean1606.4611
Median Absolute Deviation (MAD)420
Skewness17.878445
Sum4769583
Variance34609413
MonotonicityNot monotonic
2023-04-29T09:10:59.210151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
288 8
 
0.3%
272 8
 
0.3%
246 8
 
0.3%
516 7
 
0.2%
394 7
 
0.2%
Other values (1655) 2886
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
79963 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
62812 1
< 0.1%
58243 1
< 0.1%
57785 1
< 0.1%

qtde_products
Real number (ℝ)

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.70529
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:10:59.440953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.842
Coefficient of variation (CV)2.1991065
Kurtosis354.83735
Mean122.70529
Median Absolute Deviation (MAD)44
Skewness15.70614
Sum364312
Variance72814.703
MonotonicityNot monotonic
2023-04-29T09:10:59.690731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 45
 
1.5%
20 38
 
1.3%
35 35
 
1.2%
15 33
 
1.1%
29 33
 
1.1%
19 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 29
 
1.0%
Other values (459) 2630
88.6%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 27
0.9%
10 27
0.9%
ValueCountFrequency (%)
7837 1
< 0.1%
5670 1
< 0.1%
5095 1
< 0.1%
4577 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1636 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.900057
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:10:59.892579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.974384
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9343
Coefficient of variation (CV)19.979445
Kurtosis2890.7074
Mean51.900057
Median Absolute Deviation (MAD)5.9942223
Skewness53.444228
Sum154091.27
Variance1075232.8
MonotonicityNot monotonic
2023-04-29T09:11:00.080783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

diff_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.35143
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:11:00.303782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.928571
median48.285714
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.404762

Descriptive statistics

Standard deviation63.542829
Coefficient of variation (CV)0.94345182
Kurtosis4.8877032
Mean67.35143
Median Absolute Deviation (MAD)26.285714
Skewness2.062909
Sum199966.4
Variance4037.6912
MonotonicityNot monotonic
2023-04-29T09:11:00.519742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
21 17
 
0.6%
46 17
 
0.6%
11 17
 
0.6%
1 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11379122
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:11:00.750677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049418605
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033078735

Descriptive statistics

Standard deviation0.40815715
Coefficient of variation (CV)3.5868949
Kurtosis989.35782
Mean0.11379122
Median Absolute Deviation (MAD)0.012191338
Skewness24.880371
Sum337.84614
Variance0.16659226
MonotonicityNot monotonic
2023-04-29T09:11:00.989479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.02777777778 17
 
0.6%
0.0625 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2637
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtdade_itens_retornados
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:11:01.217837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2023-04-29T09:11:01.438715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

num_retornos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8386662
Minimum0
Maximum223
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:11:01.683988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile12
Maximum223
Range223
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.576352
Coefficient of variation (CV)3.0212612
Kurtosis202.71814
Mean2.8386662
Median Absolute Deviation (MAD)1
Skewness11.248531
Sum8428
Variance73.553814
MonotonicityNot monotonic
2023-04-29T09:11:01.862198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 462
 
15.6%
2 283
 
9.5%
3 170
 
5.7%
4 117
 
3.9%
5 82
 
2.8%
6 53
 
1.8%
7 52
 
1.8%
8 41
 
1.4%
11 24
 
0.8%
Other values (48) 204
 
6.9%
ValueCountFrequency (%)
0 1481
49.9%
1 462
 
15.6%
2 283
 
9.5%
3 170
 
5.7%
4 117
 
3.9%
5 82
 
2.8%
6 53
 
1.8%
7 52
 
1.8%
8 41
 
1.4%
9 18
 
0.6%
ValueCountFrequency (%)
223 1
< 0.1%
133 1
< 0.1%
112 1
< 0.1%
111 1
< 0.1%
101 1
< 0.1%
92 1
< 0.1%
90 1
< 0.1%
81 1
< 0.1%
78 1
< 0.1%
70 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.34954
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:11:02.052259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172
Q3281.5
95-th percentile599.52
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation791.50241
Coefficient of variation (CV)3.1742686
Kurtosis2256.2455
Mean249.34954
Median Absolute Deviation (MAD)82.75
Skewness44.683281
Sum740318.79
Variance626476.07
MonotonicityNot monotonic
2023-04-29T09:11:02.256901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
86 9
 
0.3%
82 9
 
0.3%
136 8
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
71 7
 
0.2%
Other values (1963) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct1010
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.155074
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T09:11:02.566791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.513033
Coefficient of variation (CV)0.88074783
Kurtosis27.694698
Mean22.155074
Median Absolute Deviation (MAD)8.2
Skewness3.4982521
Sum65778.414
Variance380.75846
MonotonicityNot monotonic
2023-04-29T09:11:02.910380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 40
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 32
 
1.1%
18 31
 
1.0%
10 30
 
1.0%
16 29
 
1.0%
17 28
 
0.9%
Other values (1000) 2622
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

Interactions

2023-04-29T09:10:53.744343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:21.773431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:23.693682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:25.538965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:27.747499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:29.859374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:32.041111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:34.434663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:36.621341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:39.829477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:42.961450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:46.558672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:50.278515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:53.972754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:21.910581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:23.836565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:25.699406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:27.869136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:30.030181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:32.270879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:34.596783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:36.790787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:40.024264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:43.266187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:46.800525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:50.526419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:54.156591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:22.035666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:23.956639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:25.874870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:27.981157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:30.187538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:32.520292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:34.761765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:36.936953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:40.228945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:43.478518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:47.093203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:50.749104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:54.343254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:22.159864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:24.129302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:26.048470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:28.125853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:30.338790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:32.712854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:34.906900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:37.077648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:40.450179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:43.761522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:47.391807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:51.023611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:54.679089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:22.287765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:24.244595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:26.249842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:28.241964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:30.491343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:32.908759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:35.090497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:37.217110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:40.673782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:44.002822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:47.796349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:51.263747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:54.920172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:22.441072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:24.378953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:26.415822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:28.416954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:30.702324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:33.129782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:35.306095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:37.889977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:40.924423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:44.276063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:48.199403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:51.561893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:55.106451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:22.580796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:24.540143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:26.575190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:28.567241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:30.897053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:33.307549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:35.463669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:38.212466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:41.179302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:44.559019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:48.595720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:51.812070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:55.315374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:22.698108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:24.668371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:26.729162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:28.724674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:31.068331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:33.446600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:35.621594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:38.550082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:41.380468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:44.829875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:48.882442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:52.004379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:55.529107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:22.834834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:24.812034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:26.912694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:28.881169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:31.229394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:33.610941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:35.825210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:38.746415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:41.645372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:45.043826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:49.111480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:52.257560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:55.716307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:22.999985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:24.976305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:27.110459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:29.032252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:31.400559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:33.798736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:35.993203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:38.958399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:41.943809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:45.298712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:49.342670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:52.477453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:55.962108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:23.130108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:25.120733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:27.299962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:29.223711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:31.564641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:33.983327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:36.179182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:39.172854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:42.229393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:45.630736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:49.600602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:52.976559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:56.174356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:23.237901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:25.241784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:27.443281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:29.553057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:31.698808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:34.120355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:36.331964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:39.360160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:42.450759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:45.869264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:49.797089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:53.203657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:56.340013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:23.547102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:25.381464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:27.596978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:29.713643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:31.854401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:34.285554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:36.478575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:39.596450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:42.712275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:46.134105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:49.996307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-29T09:10:53.487674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-29T09:11:03.114387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
customer_idmonetaryrecencyqtde_invoicesqtde_itemsqtde_productsavg_ticketdiff_daysfrequencyqtdade_itens_retornadosnum_retornosavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0760.0010.025-0.0700.013-0.1310.019-0.002-0.063-0.058-0.123-0.007
monetary-0.0761.000-0.4150.7700.9270.7440.246-0.2480.0900.3720.3780.5760.291
recency0.001-0.4151.000-0.502-0.408-0.4350.0480.1080.018-0.119-0.125-0.098-0.106
qtde_invoices0.0250.770-0.5021.0000.7170.6900.059-0.2580.0780.2930.3010.1010.025
qtde_items-0.0700.927-0.4080.7171.0000.7310.168-0.2270.0800.3440.3390.7290.321
qtde_products0.0130.744-0.4350.6900.7311.000-0.377-0.1660.0360.2420.2730.3840.699
avg_ticket-0.1310.2460.0480.0590.168-0.3771.000-0.1220.0910.1900.1530.189-0.611
diff_days0.019-0.2480.108-0.258-0.227-0.166-0.1221.000-0.881-0.396-0.413-0.0770.048
frequency-0.0020.0900.0180.0780.0800.0360.091-0.8811.0000.2340.2370.027-0.072
qtdade_itens_retornados-0.0630.372-0.1190.2930.3440.2420.190-0.3960.2341.0000.9550.2110.019
num_retornos-0.0580.378-0.1250.3010.3390.2730.153-0.4130.2370.9551.0000.1940.059
avg_basket_size-0.1230.576-0.0980.1010.7290.3840.189-0.0770.0270.2110.1941.0000.448
avg_unique_basket_size-0.0070.291-0.1060.0250.3210.699-0.6110.048-0.0720.0190.0590.4481.000

Missing values

2023-04-29T09:10:56.593172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-29T09:10:57.021837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idmonetaryrecencyqtde_invoicesqtde_itemsqtde_productsavg_ticketdiff_daysfrequencyqtdade_itens_retornadosnum_retornosavg_basket_sizeavg_unique_basket_size
0178505391.21372.0034.001733.00297.0018.1535.5017.0040.0015.0050.978.74
1130473232.5956.009.001390.00171.0018.9027.250.0335.0023.00154.4419.00
2125836705.382.0015.005028.00232.0028.9023.190.0450.003.00335.2015.47
313748948.2595.005.00439.0028.0033.8792.670.020.000.0087.805.60
415100876.00333.003.0080.003.00292.008.600.0722.003.0026.671.00
5152914623.3025.0014.002102.00102.0045.3323.200.0429.006.00150.147.29
6146885630.877.0021.003621.00327.0017.2218.300.06399.0032.00172.4315.57
7178095411.9116.0012.002057.0061.0088.7235.700.0341.002.00171.425.08
81531160767.900.0091.0038194.002379.0025.544.140.24474.00112.00419.7126.14
9160982005.6387.007.00613.0067.0029.9347.670.020.000.0087.579.57
customer_idmonetaryrecencyqtde_invoicesqtde_itemsqtde_productsavg_ticketdiff_daysfrequencyqtdade_itens_retornadosnum_retornosavg_basket_sizeavg_unique_basket_size
5627177271060.2515.001.00645.0066.0016.066.001.006.001.00645.0066.00
563717232421.522.002.00203.0036.0011.7112.000.150.000.00101.5018.00
563817468137.0010.002.00116.005.0027.404.000.400.000.0058.002.50
564913596697.045.002.00406.00166.004.207.000.250.000.00203.0083.00
5655148931237.859.002.00799.0073.0016.962.000.670.000.00399.5036.50
565912479473.2011.001.00382.0030.0015.774.001.0034.004.00382.0030.00
568014126706.137.003.00508.0015.0047.083.000.7550.001.00169.335.00
5686135211092.391.003.00733.00435.002.514.500.300.000.00244.33145.00
569615060301.848.004.00262.00120.002.521.002.000.000.0065.5030.00
571512558269.967.001.00196.0011.0024.546.001.00196.0011.00196.0011.00